Evidence map›Paper›PMID 40522564›Full record

ArticleJournal of neuro-oncology2025

Decoding meningioma prognosis with multi-omics: macrophage diversity, immune-CNV interplay, and novel SPP1-targeted strategies.

Hailang Fan, Xiaojie Li, Yaqian Zhao, Lairong Song, Lanlan Sun, Zhen Wu, Junting Zhang, Haikun Zhang, Feifan Zhang, Yingdi Huang and 2 more

Abstract read
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In one paragraph

Article in Journal of neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. SPP1Cell death & disease · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Hailang Fan *Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Engineering Medicine, Beihang University, Beijing, 100191, P. R. China.
Xiaojie Li *Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, P. R. China.
Yaqian ZhaoKey Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Engineering Medicine, Beihang University, Beijing, 100191, P. R. China.
Lairong SongDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, P. R. China.
Lanlan SunKey Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Engineering Medicine, Beihang University, Beijing, 100191, P. R. China.
Zhen WuDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, P. R. China.
Junting ZhangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, P. R. China.
Haikun ZhangCenter for Human Genomics and Precision Medicine, Department of Surgery, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, 53705, USA.
Feifan ZhangLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 639798, Singapore.
Yingdi HuangKey Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Engineering Medicine, Beihang University, Beijing, 100191, P. R. China.
Dake ZhangKey Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Engineering Medicine, Beihang University, Beijing, 100191, P. R. China. dakezhang@buaa.edu.cn.
Liang WangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, P. R. China. wangliang@bjtth.org.

Funding

Higher Education Discipline Innovation Project 111 Project No. B13003Natural Science Foundation of Beijing J180005 and 7192056Ningxia Municipal Natural Science Foundation 2024AAC03439
6 · The paper itself

Abstract

backgroundMeningiomas exhibit significant tumor heterogeneity leading to diverse prognosis of patients. While DNA methylation (DNAme)-based classification has shown good performance in subtyping of meningiomas, single-cell transcriptomics offer an opportunity to further explore cell population activity. Understanding the functional states of macrophage populations is especially critical in meningiomas, given their established roles in tumor progression and therapeutic response.

methodsWe performed integrated multi-omics clustering of DNAme and bulk RNA-seq data from 302 paired meningioma samples. This identified molecular types characterized by distinct survival, copy number variation (CNV), immune infiltration, and pathway enrichment profiles. Single-cell RNA-seq (scRNA-seq) validated CNV findings and enabled deconvolution of type-specific macrophage populations using SCISSOR, while scRank assessed candidate genes to identify therapeutic targets.

resultsWe identified five meningioma molecular types (CS1-CS5) with distinct recurrence risks, survival, CNV landscapes, and immune microenvironments: Proliferative (CS1), Metabolic (CS2), Mixed (CS3), Immune-Enriched (CS4), and NF2-Wild-Type (CS5). CS4 was featured by high macrophage infiltration, while CS5 showed immune-desert characteristics. Type-specific CNV profiles showed chromosome 22 deletions relating to immune infiltration, and SPP1-centered gene networks highlighted its regulatory role in macrophage plasticity. Particularly, SPP1 upregulation associated with poor prognosis in CS1 and downregulation likely contributed to better outcomes in CS4. Drug sensitivity analyses identified the potential of targeting SPP1 for macrophage reprogramming in future type-directed therapies.

conclusionOur meningioma prognostic stratification characterized macrophage functional heterogeneity and CNV-driven immune microenvironment disparities. We found SPP1 as a critical regulator for macrophage function and offering a promising candidate for future treatment to target in an aggressive molecular type.

Indexed as

Biomarkers, TumorDNA Copy Number VariationsMacrophagesMeningeal NeoplasmsMeningiomaDNA MethylationFemaleHumansMaleMultiomicsPrognosisTumor MicroenvironmentBiomarkers, TumorBulk and single-cell RNA-seqDNA methylationMacrophagesMeningiomasMolecular classificationSPP1

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.